Developing a Keystroke Biometric System for Continual Authentication of Computer Users

Monaco, John V.; Bakelman, Ned; Cha, Sung-Hyuk; Tappert, Charles C.

doi:10.1109/eisic.2012.58

Cited by 36 publications

(12 citation statements)

References 19 publications

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“…Early work in the field used keystroke dynamics [29], [18], [45], [19] to authenticate desktop users. Later studies on desktop users demonstrated the feasibility of using a variety of behavioral traits, including mouse dynamics [54], softbiometrics [47], hand movement [50], keyboard acoustics [51], screen fingerprints [21], language use [61], [49] and cognition during text production [39], [43], [60], [44]. Early studies in continuous authentication of mobile phone users focused on keystroke dynamics (see [14], [13], [9], [41], [10]), because these devices had a hardware keyboard to interface with the user.…”

Section: Evolution Of Continuous Authentication In Desktops Andmentioning

confidence: 99%

Section: Evolution Of Continuous Authentication In Desktops Andmentioning

confidence: 99%

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HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

Sitová

Šeděnka

Yang

et al. 2016

IEEE Trans.Inform.Forensic Secur.

321

178

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We introduce Hand Movement, Orientation, and Grasp (HMOG), a set of behavioral features to continuously authenticate smartphone users. HMOG features unobtrusively capture subtle micro-movement and orientation dynamics resulting from how a user grasps, holds, and taps on the smartphone. We evaluated authentication and biometric key generation (BKG) performance of HMOG features on data collected from 100 subjects typing on a virtual keyboard. Data was collected under two conditions: sitting and walking. We achieved authentication EERs as low as 7.16% (walking) and 10.05% (sitting) when we combined HMOG, tap, and keystroke features. We performed experiments to investigate why HMOG features perform well during walking. Our results suggest that this is due to the ability of HMOG features to capture distinctive body movements caused by walking, in addition to the hand-movement dynamics from taps. With BKG, we achieved EERs of 15.1% using HMOG combined with taps. In comparison, BKG using tap, key hold, and swipe features had EERs between 25.7% and 34.2%. We also analyzed the energy consumption of HMOG feature extraction and computation. Our analysis shows that HMOG features extracted at 16Hz sensor sampling rate incurred a minor overhead of 7.9% without sacrificing authentication accuracy. Two points distinguish our work from current literature: 1) we present the results of a comprehensive evaluation of three types of features (HMOG, keystroke, and tap) and their combinations under the same experimental conditions; and 2) we analyze the features from three perspectives (authentication, BKG, and energy consumption on smartphones).

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Section: Evolution Of Continuous Authentication In Desktops Andmentioning

confidence: 99%

Section: Evolution Of Continuous Authentication In Desktops Andmentioning

confidence: 99%

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

Sitová

Šeděnka

Yang

et al. 2016

IEEE Trans.Inform.Forensic Secur.

321

178

View full text Add to dashboard Cite

show abstract

“…The open Villani keystroke dataset (Tappert et al, 2009;Monaco, Bakelman, Cha, & Tappert, 2012) is a keystroke dataset collected in an experimental setting. In the experiment, students and faculty could choose to conduct a copy task and/or an email writing task.…”

Section: Villani Datasetmentioning

confidence: 99%

Understanding the keystroke log: the effect of writing task on keystroke features

2019

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Keystroke logging is used to automatically record writers' unfolding typing process and to get insight into moments when they struggle composing text. However, it is not clear which and how features from the keystroke log map to higher-level cognitive processes, such as planning and revision. This study aims to investigate the sensitivity of frequently used keystroke features across tasks with different cognitive demands. Two keystroke datasets were analyzed: one consisting of a copy task and an email writing task, and one with a larger difference in cognitive demand: a copy task and an academic summary task. The differences across tasks were modeled using Bayesian linear mixed effects models. Posterior distributions were used to compare the strength and direction of the task effects across features and datasets. The results showed that the average of all interkeystroke intervals were found to be stable across tasks. Features related to the time between words and (sub)sentences only differed between the copy and the academic task. Lastly, keystroke features related to the number of words, revisions, and total time, differed across tasks in both datasets. To conclude, our results indicate that the latter features are related to cognitive load or task complexity. In addition, our research shows that keystroke features are sensitive to small differences in the writing tasks at hand.

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“…This means that, for example, a system where the user must present his or her fingerprint on a scanner, or a system where he or she needs to speak specific words, is not a good system, because these interfere with the normal daily routine. In most cases, when a CA system is applied to a computer, mouse dynamics [2][3][4][5] and/or keystroke dynamics (KDs) [1, [6][7][8][9][10] have been used, although it is very well possible to include face recognition in case a webcam is available. For simplicity sake , in this paper, we will often assume that the CA system is based on KDs to help explain the ideas more concretely.…”

Section: Introductionmentioning

confidence: 99%

Performance evaluation of continuous authentication systems

Bours

Mondal

2015

IET biom.

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In this study, the authors will describe how performance results for continuous authentication (CA) should be reported. Most research on alleged CA is in fact periodic authentication, and performance is then reported in false match and false non-match rates. Here the authors will describe average number of impostor or genuine actions as the performance indicators, and will describe a more detailed performance reporting method. The authors' current results have been reported in continuous authentication, based on analysis performed on two different datasets, and compared those results to the best results in comparable research, where they show that their results outperform most other known results.

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Developing a Keystroke Biometric System for Continual Authentication of Computer Users

Cited by 36 publications

References 19 publications

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

HMOG: New Behavioral Biometric Features for Continuous Authentication of Smartphone Users

Understanding the keystroke log: the effect of writing task on keystroke features

Performance evaluation of continuous authentication systems

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